Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-20
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory.
Further, a self-tuning weighted measurement fusion Kalman filter is presented.
The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics.
The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics.
It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality.
The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.
American Psychological Association (APA)
Wang, Xin& Sun, Shu-Li. 2012. Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises. Journal of Applied Mathematics،Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-993149
Modern Language Association (MLA)
Wang, Xin& Sun, Shu-Li. Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises. Journal of Applied Mathematics No. 2012 (2012), pp.1-16.
https://search.emarefa.net/detail/BIM-993149
American Medical Association (AMA)
Wang, Xin& Sun, Shu-Li. Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises. Journal of Applied Mathematics. 2012. Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-993149
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-993149